Contemporary Advances in Industry 4.0 Technologies and Theories for Manufacturing Sustainability
Bibliographic record
Abstract
Riding on the machines that changed the world, industry has undergone numerous revolutions—from the initial steam-engine-powered machines to the advent of electricity in industrial processes for mass production; then the automated machines, which involved advanced electronics and information technologies in automating the production process; and today, the fourth Industrial Revolution (Industry 4.0) which integrates smart machines with digital technologies to maximize industrial productivity. Considering the seminal report on ‘Our Common Future’ as a corporate sustainability reference, the common revolutions have also appeared in manufacturing. This ranges from the traditional substitution-based manufacturing to the advent of lean manufacturing for reducing waste and creating value in production processes, which is being followed with green manufacturing, which involves the 3R (reduce, reuse, and recycle) concept in greening product supply chains, and, today, sustainable manufacturing, which takes sustainability issues into three interrelated compartments—product, process, and system—using a broader innovation-based 6R methodology to not only meet the 3Rs but also to remanufacture, redesign, and recover the products over multiple life cycles.There are many significant efforts to develop sustainability in the manufacturing industry; however, the development is generally traced by compartmentalizing the manufacturing’s integral elements (i.e., products, processes, and systems), which requires (1) a paradigm shift from single life cycle, open loop to multiple life cycle, closed loop at the product level; (2) the optimization of technological advancements and process planning to reduce energy and resource intake, toxic wastes, and occupational hazards, and to improve product life via the manipulation of process-driven surface integrity at the process level; and (3) the integration of the entire supply chain, from the major life-cycle stages to the multiple life cycles at the system level. It is contended that Industry 4.0 and its enabling technologies and principles give the unique opportunity to move towards this end.This Special Issue is aimed at presenting contemporary advances in Industry 4.0 technologies and theories for manufacturing sustainability. It intends to cover the biocomplexity of the environment and associated technological challenges facing the needs of society for economic growth and prosperity as applied to the design and manufacturing of discrete products. We hereby invite authors to submit original research and critical review articles developing sustainability into Industry 4.0 technologies and theories throughout all levels of manufacturing—from pre-manufacturing, manufacturing, and use through post-use stages in the life cycle—to enable a shift in the manufacturing context towards a more sustainability-based state.Link: https://www.mdpi.com/si/108260
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".